We lifted 9 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| feizc/fluxmusic | canonical | 6 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| get_padding | Ran | feizc/fluxmusic/audioldm2/hifigan/models.py pointer only (licence: NONE) · get_code("a26f85d7c72ef39a") |
| get_param_num | Ran | feizc/fluxmusic/audioldm2/utilities/model.py pointer only (licence: NONE) · get_code("f1cba67eb6d6e4ad") |
| prepare | Ran | feizc/fluxmusic/sample.py pointer only (licence: NOASSERTION) · get_code("fb7ac27a448677a1") |
| rope | Ran | feizc/fluxmusic/modules/layers.py pointer only (licence: NONE) · get_code("17724d4203fa5863") |
| swish | Ran | feizc/fluxmusic/modules/autoencoder.py pointer only (licence: NONE) · get_code("5fb036462ae82f9f") |
| torch_version_orig_mod_remove | Ran | feizc/fluxmusic/audioldm2/utilities/model.py pointer only (licence: NOASSERTION) · get_code("4dab43e0c6c8c170") |
| apply_rope | Not yet run | feizc/fluxmusic/modules/layers.py pointer only (licence: NONE) · get_code("3444bbcc9065f707") |
| attention | Not yet run | feizc/fluxmusic/modules/layers.py pointer only (licence: NONE) · get_code("b17d74354ecaf7fe") |
| get_available_checkpoint_keys | Not yet run | feizc/fluxmusic/audioldm2/utilities/model.py pointer only (licence: NOASSERTION) · get_code("89bb9be7815a9834") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper explores a simple extension of diffusion-based rectified flow Transformers for text-to-music generation, termed as FluxMusic. Generally, along with design in advanced Flux\footnote{https://github.com/black-forest-labs/flux} model, we transfers it into a latent VAE space of mel-spectrum. It involves first applying a sequence of independent attention to the double text-music stream, followed by a stacked single music stream for denoised patch prediction. We employ multiple pre-trained text encoders to sufficiently capture caption semantic information as well as inference flexibility. In between, coarse textual information, in conjunction with time step embeddings, is utilized in a modulation mechanism, while fine-grained textual details are concatenated with the music patch sequence as inputs. Through an in-depth study, we demonstrate that rectified flow training with an optimized architecture significantly outperforms established diffusion methods for the text-to-music task, as evidenced by various automatic metrics and human preference evaluations. Our experimental data, code, and model weights are made publicly available at: \url{https://github.com/feizc/FluxMusic}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2409.00587")
get_code_for_paper("2409.00587")
have("2409.00587")
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